Listed below are some of the jointly mentored projects for which faculty from CTCN-participating departments are currently recruiting CTCN postdoctoral scholars. CTCN Postdoctoral Fellowship applicants are encouraged to either express an interest in one of these, or propose new projects spanning the interests of CTCN faculty.
CTCN Faculty: please send new project proposals to ctcn@wustl.edu
Unraveling the mechanisms of sleep function
PIs: Yao Chen, PhD (Department of Neuroscience) and Tao Ju, PhD (Department of Computer Science & Engineering)
Sleep is universal, important, yet enigmatic. Using optical methods to analyze and perturb biological signals in real time in mice across behavior states, and using computational methods to automate behavior motif identification and perform time-series analysis, we seek to elucidate how biological signals inside the cell contribute to the functions of sleep and other behavior states.
Developing ‘Google Translate’ for neuroimaging brain representations
PIs: Ulugbek Kamilov, PhD (Departments of Computer Science & Engineering and Electrical & Systems Engineering) and Janine Bijsterbosch, PhD (Department of Radiology)
We will develop novel generative non-convolutional deep learning algorithms to translate neuroimaging brain representations.
To determine the constraints of brain-like surface representations in artificial neural networks
PIs: Tom P. Franken, MD, PhD (Department of Neuroscience), Ralf Wessel, PhD (Department of Physics), Nathan Jacobs, PhD (Department of Computer Science & Engineering)
Deep convolutional neural networks exceed human performance on object recognition tasks, but focus on textures and local features rather than the global shapes that dominate human perception. In this project we will leverage the power of artificial neural networks to explore which architectural and training conditions lead to brain-like object representations.
Impact of arousal and sleep states on fluctuations in time-varying functional connectivity
PIs: Muriah Wheelock, PhD (Department of Radiology) and Likai Chen, PhD (Department of Mathematics & Statistics)
This project will use modern change-point detection techniques to estimate time-resolved fluctuations in functional connectivity using concurrent EEG-fMRI data from adults and fMRI data from both adult and baby participants in the Human/Baby Connectome Project (HCP/BCP). The goal of this project is to determine the impact of arousal and sleep states on fluctuations in time-varying functional connectivity, including those driven by variability in respiration and heart rate.